Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

5-2016

Abstract

Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.

Keywords

Euclidean, High-dimensional, Information preservation, Low-dimensional representation, Objective metrics, Ordinal data, data visualization, data mining

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, May 5-7

First Page

396

Last Page

404

ISBN

9781611974348

Identifier

10.1137/1.9781611974348.45

Publisher

SIAM

City or Country

Philadelphia, PA

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1137/1.9781611974348.45

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